Abstract
Diffusion models have achieved remarkable success in imaging inverse problems owing to their powerful generative capabilities. However, existing approaches typically rely on models trained for specific degradation types, limiting their generalizability to various degradation scenarios. To address this limitation, we propose a zero-shot framework capable of handling various imaging inverse problems without model retraining. We introduce a likelihood-guided noise refinement mechanism that derives a closed-form approximation of the likelihood score, simplifying score estimation and avoiding expensive gradient computations. This estimated score is subsequently utilized to refine the model-predicted noise, thereby better aligning the restoration process with the generative framework of diffusion models. In addition, we integrate the Denoising Diffusion Implicit Models (DDIM) sampling strategy to further improve inference efficiency. The proposed mechanism can be applied to both optimization-based and sampling-based schemes, providing an effective and flexible zero-shot solution for imaging inverse problems. Extensive experiments demonstrate that our method achieves superior performance across multiple inverse problems, particularly in compressive sensing, delivering high-quality reconstructions even at an extremely low sampling rate (5%).
Abstract (translated)
扩散模型在成像逆问题中取得了显著的成功，这得益于其强大的生成能力。然而，现有的方法通常依赖于针对特定退化类型训练的模型，从而限制了它们在各种退化场景中的泛化能力。为了解决这一局限性，我们提出了一种零样本框架，该框架能够在不重新训练模型的情况下处理多种成像逆问题。我们引入了一个基于似然性的噪声精炼机制，它可以推导出一个闭式形式的似然分数近似，简化了分数估计过程，并避免了昂贵的梯度计算。这个估计出来的分数随后被用来改进模型预测的噪声，从而更好地使恢复过程与扩散模型的生成框架相一致。此外，我们将去噪扩散隐式模型（DDIM）采样策略集成进来，进一步提高了推理效率。所提出的机制既可以应用于基于优化的方法也可以用于基于抽样的方法，为成像逆问题提供了一个有效且灵活的零样本解决方案。广泛的实验表明，我们的方法在多个逆问题上实现了优越的表现，特别是在压缩感知领域，在极低的采样率（5%）下也能实现高质量的重建。
URL
https://arxiv.org/abs/2506.13391
